一句话：提出以任务为中心的对话式助手，用 JSON 元数据帮助非专家快速原型化深度学习应用。
深度神经网络已成为众多行业软件方案的核心，但其依赖数据学习的内部机制对非专家而言既有用又神秘。为了让更广泛人群能够设计、开发并探索这类解决方案，本文提出一种新的辅助式方法：以待执行的下游任务为中心，帮助实践者开始使用和应用深度学习技术。该方法将知识外化，用 JSON 文件组织流水线元数据及其相关工件（如模型代码、待加载数据集、较优超参数选择），用户在与对话代理交互时获得针对给定任务的候选解决方案建议。作者用初始测试床 UI 原型支撑该提案，展示了交互与知识组织方式，但摘要未报告系统性用户评估或性能结果。
Machine learning (ML) systems based on deep neural networks are more present than ever in software solutions for numerous industries. Their inner workings relying on models learning with data are as helpful as they are mysterious for non-expert people. There is an increasing need to make the design and development of those solutions accessible to a more general public while at the same time making them easier to explore. In this paper, to address this need, we discuss a proposition of a new assisted approach, centered on the downstream task to be performed, for helping practitioners to start using and applying Deep Learning (DL) techniques. This proposal, supported by an initial testbed UI prototype, uses an externalized form of knowledge, where JSON files compile different pipeline metadata information with their respective related artifacts (e.g., model code, the dataset to be loaded, good hyperparameter choices) that are presented as the user interacts with a conversational agent to suggest candidate solutions for a given task.
@inproceedings{paperbot7146,
title = {Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition},
author = {Gustavo Rodrigues dos Reis and Adrian Mos and Mario Cortes-Cornax and Cyril Labbé},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3561166}
}